Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions.

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

miR-223 Exerts Translational Control of Proatherogenic Genes in Macrophages.

BACKGROUND: A significant burden of atherosclerotic disease is driven by inflammation. Recently, microRNAs (miRNAs) have emerged as important factors driving and protecting from atherosclerosis. miR-223 regulates cholesterol metabolism and inflammation via targeting both cholesterol biosynthesis pathway and NFkB signaling pathways; however, its role in atherosclerosis has not been investigated. We hypothesize that miR-223 globally regulates core inflammatory pathways in macrophages in response to inflammatory and atherogenic stimuli thus limiting the progression of atherosclerosis. METHODS AND RESULTS: Loss of miR-223 in macrophages decreases Abca1 gene and protein expression as well as cholesterol efflux to apoA1 (Apolipoprotein A1) and enhances proinflammatory gene expression. In contrast, overexpression of miR-223 promotes the efflux of cholesterol and macrophage polarization toward an anti-inflammatory phenotype. These beneficial effects of miR-223 are dependent on its target gene, the transcription factor Sp3. Consistent with the antiatherogenic effects of miR-223 in vitro, mice receiving miR223-/- bone marrow exhibit increased plaque size, lipid content, and circulating inflammatory cytokines (ie, IL-1ß). Deficiency of miR-223 in bone marrow-derived cells also results in an increase in circulating pro-atherogenic cells (total monocytes and neutrophils) compared with control mice. Furthermore, the expression of miR-223 target gene (Sp3) and pro-inflammatory marker (Il-6) are enhanced whereas the expression of Abca1 and anti-inflammatory marker (Retnla) are reduced in aortic arches from mice lacking miR-223 in bone marrow-derived cells. In mice fed a high-cholesterol diet and in humans with unstable carotid atherosclerosis, the expression of miR-223 is increased. To further understand the molecular mechanisms underlying the effect of miR-223 on atherosclerosis in vivo, we characterized global RNA translation profile of macrophages isolated from mice receiving wild-type or miR223-/- bone marrow. Using ribosome profiling, we reveal a notable upregulation of inflammatory signaling and lipid metabolism at the translation level but less significant at the transcription level. Analysis of upregulated genes at the translation level reveal an enrichment of miR-223-binding sites, confirming that miR-223 exerts significant changes in target genes in atherogenic macrophages via altering their translation. CONCLUSIONS: Our study demonstrates that miR-223 can protect against atherosclerosis by acting as a global regulator of RNA translation of cholesterol efflux and inflammation pathways.

PersonaDrive: a method for the identification and prioritization of personalized cancer drivers.

MOTIVATION: A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. RESULTS: We propose a novel method that integrates genomic data, biological pathways and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted 'pairwise pathway coverage' scores across all the samples, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against five state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell line data. In addition, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines' reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information toward the development of personalized treatments and therapies. AVAILABILITY AND IMPLEMENTATION: All the codes and data are available at https://github.com/abu-compbio/PersonaDrive, and the data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.6520187. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers.

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

Ranking cancer drivers via betweenness-based outlier detection and random walks.

BACKGROUND: Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. RESULTS: We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. CONCLUSIONS: Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

Sparse data embedding and prediction by tropical matrix factorization.

BACKGROUND: Matrix factorization methods are linear models, with limited capability to model complex relations. In our work, we use tropical semiring to introduce non-linearity into matrix factorization models. We propose a method called Sparse Tropical Matrix Factorization (STMF) for the estimation of missing (unknown) values in sparse data. RESULTS: We evaluate the efficiency of the STMF method on both synthetic data and biological data in the form of gene expression measurements downloaded from The Cancer Genome Atlas (TCGA) database. Tests on unique synthetic data showed that STMF approximation achieves a higher correlation than non-negative matrix factorization (NMF), which is unable to recover patterns effectively. On real data, STMF outperforms NMF on six out of nine gene expression datasets. While NMF assumes normal distribution and tends toward the mean value, STMF can better fit to extreme values and distributions. CONCLUSION: STMF is the first work that uses tropical semiring on sparse data. We show that in certain cases semirings are useful because they consider the structure, which is different and simpler to understand than it is with standard linear algebra.

DriveWays: a method for identifying possibly overlapping driver pathways in cancer.

The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay's output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.

MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules.

MOTIVATION: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. RESULTS: We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MITF-MIR211 axis is a novel autophagy amplifier system during cellular stress.

Macroautophagy (autophagy) is an evolutionarily conserved recycling and stress response mechanism. Active at basal levels in eukaryotes, autophagy is upregulated under stress providing cells with building blocks such as amino acids. A lysosome-integrated sensor system composed of RRAG GTPases and MTOR complex 1 (MTORC1) regulates lysosome biogenesis and autophagy in response to amino acid availability. Stress-mediated inhibition of MTORC1 results in the dephosphorylation and nuclear translocation of the TFE/MITF family of transcriptional factors, and triggers an autophagy- and lysosomal-related gene transcription program. The role of family members TFEB and TFE3 have been studied in detail, but the importance of MITF proteins in autophagy regulation is not clear so far. Here we introduce for the first time a specific role for MITF in autophagy control that involves upregulation of MIR211. We show that, under stress conditions including starvation and MTOR inhibition, a MITF-MIR211 axis constitutes a novel feed-forward loop that controls autophagic activity in cells. Direct targeting of the MTORC2 component RICTOR by MIR211 led to the inhibition of the MTORC1 pathway, further stimulating MITF translocation to the nucleus and completing an autophagy amplification loop. In line with a ubiquitous function, MITF and MIR211 were co-expressed in all tested cell lines and human tissues, and the effects on autophagy were observed in a cell-type independent manner. Thus, our study provides direct evidence that MITF has rate-limiting and specific functions in autophagy regulation. Collectively, the MITF-MIR211 axis constitutes a novel and universal autophagy amplification system that sustains autophagic activity under stress conditions. Abbreviations: ACTB: actin beta; AKT: AKT serine/threonine kinase; AKT1S1/PRAS40: AKT1 substrate 1; AMPK: AMP-activated protein kinase; ATG: autophagy-related; BECN1: beclin 1; DEPTOR: DEP domain containing MTOR interacting protein; GABARAP: GABA type A receptor-associated protein; HIF1A: hypoxia inducible factor 1 subunit alpha; LAMP1: lysosomal associated membrane protein 1; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MAPKAP1/SIN1: mitogen-activated protein kinase associated protein 1; MITF: melanogenesis associated transcription factor; MLST8: MTOR associated protein, LST8 homolog; MRE: miRNA response element; MTOR: mechanistic target of rapamycin kinase; MTORC1: MTOR complex 1; MTORC2: MTOR complex 2; PRR5/Protor 1: proline rich 5; PRR5L/Protor 2: proline rich 5 like; RACK1: receptor for activated C kinase 1; RPTOR: regulatory associated protein of MTOR complex 1; RICTOR: RPTOR independent companion of MTOR complex 2; RPS6KB/p70S6K: ribosomal protein S6 kinase; RT-qPCR: quantitative reverse transcription-polymerase chain reaction; SQSTM1: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TFE3: transcription factor binding to IGHM enhancer 3; TFEB: transcription factor EB; TSC1/2: TSC complex subunit 1/2; ULK1: unc-51 like autophagy activating kinase 1; UVRAG: UV radiation resistance associated; VIM: vimentin; VPS11: VPS11, CORVET/HOPS core subunit; VPS18: VPS18, CORVET/HOPS core subunit; WIPI1: WD repeat domain, phosphoinositide interacting 1.

Predicting clinical outcomes in neuroblastoma with genomic data integration.

BACKGROUND: Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis. RESULTS: Our supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions. CONCLUSION: Altogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients. REVIEWERS: This article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

Modeling Gene Regulation in Liver Hepatocellular Carcinoma with Random Forests.

Liver hepatocellular carcinoma (HCC) remains a leading cause of cancer-related death. Poor understanding of the mechanisms underlying HCC prevents early detection and leads to high mortality. We developed a random forest model that incorporates copy-number variation, DNA methylation, transcription factor, and microRNA binding information as features to predict gene expression in HCC. Our model achieved a highly significant correlation between predicted and measured expression of held-out genes. Furthermore, we identified potential regulators of gene expression in HCC. Many of these regulators have been previously found to be associated with cancer and are differentially expressed in HCC. We also evaluated our predicted target sets for these regulators by making comparison with experimental results. Lastly, we found that the transcription factor E2F6, one of the candidate regulators inferred by our model, is predictive of survival rate in HCC. Results of this study will provide directions for future prospective studies in HCC.

Inferring RBP-Mediated Regulation in Lung Squamous Cell Carcinoma.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation of mRNAs. Dysregulations in RBP-mediated mechanisms have been found to be associated with many steps of cancer initiation and progression. Despite this, previous studies of gene expression in cancer have ignored the effect of RBPs. To this end, we developed a lasso regression model that predicts gene expression in cancer by incorporating RBP-mediated regulation as well as the effects of other well-studied factors such as copy-number variation, DNA methylation, TFs and miRNAs. As a case study, we applied our model to Lung squamous cell carcinoma (LUSC) data as we found that there are several RBPs differentially expressed in LUSC. Including RBP-mediated regulatory effects in addition to the other features significantly increased the Spearman rank correlation between predicted and measured expression of held-out genes. Using a feature selection procedure that accounts for the adaptive search employed by lasso regularization, we identified the candidate regulators in LUSC. Remarkably, several of these candidate regulators are RBPs. Furthermore, majority of the candidate regulators have been previously found to be associated with lung cancer. To investigate the mechanisms that are controlled by these regulators, we predicted their target gene sets based on our model. We validated the target gene sets by comparing against experimentally verified targets. Our results suggest that the future studies of gene expression in cancer must consider the effect of RBP-mediated regulation.

Quaking promotes monocyte differentiation into pro-atherogenic macrophages by controlling pre-mRNA splicing and gene expression.

A hallmark of inflammatory diseases is the excessive recruitment and influx of monocytes to sites of tissue damage and their ensuing differentiation into macrophages. Numerous stimuli are known to induce transcriptional changes associated with macrophage phenotype, but posttranscriptional control of human macrophage differentiation is less well understood. Here we show that expression levels of the RNA-binding protein Quaking (QKI) are low in monocytes and early human atherosclerotic lesions, but are abundant in macrophages of advanced plaques. Depletion of QKI protein impairs monocyte adhesion, migration, differentiation into macrophages and foam cell formation in vitro and in vivo. RNA-seq and microarray analysis of human monocyte and macrophage transcriptomes, including those of a unique QKI haploinsufficient patient, reveal striking changes in QKI-dependent messenger RNA levels and splicing of RNA transcripts. The biological importance of these transcripts and requirement for QKI during differentiation illustrates a central role for QKI in posttranscriptionally guiding macrophage identity and function.

Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation.

Recent studies show that RNA-binding proteins (RBPs) and microRNAs (miRNAs) function in coordination with each other to control post-transcriptional regulation (PTR). Despite this, the majority of research to date has focused on the regulatory effect of individual RBPs or miRNAs. Here, we mapped both RBP and miRNA binding sites on human 3'UTRs and utilized this collection to better understand PTR. We show that the transcripts that lack competition for HuR binding are destabilized more after HuR depletion. We also confirm this finding for PUM1(2) by measuring genome-wide expression changes following the knockdown of PUM1(2) in HEK293 cells. Next, to find potential cooperative interactions, we identified the pairs of factors whose sites co-localize more often than expected by random chance. Upon examining these results for PUM1(2), we found that transcripts where the sites of PUM1(2) and its interacting miRNA form a stem-loop are more stabilized upon PUM1(2) depletion. Finally, using dinucleotide frequency and counts of regulatory sites as features in a regression model, we achieved an AU-ROC of 0.86 in predicting mRNA half-life in BEAS-2B cells. Altogether, our results suggest that future studies of PTR must consider the combined effects of RBPs and miRNAs, as well as their interactions.

LIN28 binds messenger RNAs at GGAGA motifs and regulates splicing factor abundance.

LIN28 is a conserved RNA-binding protein implicated in pluripotency, reprogramming, and oncogenesis. It was previously shown to act primarily by blocking let-7 microRNA (miRNA) biogenesis, but here we elucidate distinct roles of LIN28 regulation via its direct messenger RNA (mRNA) targets. Through crosslinking and immunoprecipitation coupled with high-throughput sequencing (CLIP-seq) in human embryonic stem cells and somatic cells expressing exogenous LIN28, we have defined discrete LIN28-binding sites in a quarter of human transcripts. These sites revealed that LIN28 binds to GGAGA sequences enriched within loop structures in mRNAs, reminiscent of its interaction with let-7 miRNA precursors. Among LIN28 mRNA targets, we found evidence for LIN28 autoregulation and also direct but differing effects on the protein abundance of splicing regulators in somatic and pluripotent stem cells. Splicing-sensitive microarrays demonstrated that exogenous LIN28 expression causes widespread downstream alternative splicing changes. These findings identify important regulatory functions of LIN28 via direct mRNA interactions.